Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning.

Simon Dedman,Rick Officer,David G Reid,Maurice Clarke,Deirdre Brophy,James P Meador

doi:10.1371/journal.pone.0188955

Simon Dedman, Rick Officer + Show 4 more

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https://doi.org/10.1371/journal.pone.0188955

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Journal: PloS one	Publication Date: Dec 7, 2017
Citations: 23	License type: CC BY 4.0

Affiliation: Galway-Mayo Institute of Technology, Marine Institute

Abstract

Boosted Regression Trees. Excellent for data-poor spatial management but hard to useMarine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage.BRTs automated and simplified for accessible general use with rich feature setWe have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement.Gbm.auto for management in various settingsBy bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios.

Highlights

Spatial management of data-limited speciesSome of the key barriers to implementation of scientific research are accessibility of evidence, quality of evidence, and organisational capacity/resources [1]
Marine spatial management typically involves the selection of appropriate Marine Protected Areas (MPAs) [3]
There is a need for a Decision Support Tool (DST) that can generate MPA candidates across a whole region, which weigh harvest-limit-based conservation against quantified displacement of the stressor, e.g. fishing effort, catch per unit effort (CPUE), or profit

Summary

Introduction

Some of the key barriers to implementation of scientific research are accessibility of evidence, quality of evidence, and organisational capacity/resources [1]. Fisheries managers and scientists recommend various spatial management tools to support MPA selection [4,5,6] These methods generally involve predictive mapping of species distribution and abundance in relation to available habitat and human activities such as fishing There is a need for a DST that can generate MPA candidates across a whole region, which weigh harvest-limit-based conservation against quantified displacement of the stressor, e.g. fishing effort, catch per unit effort (CPUE), or profit This would allow much-needed [43] evaluation of trade-offs within a framework of scientist, manager, and stakeholder discussion. A review of 39 MPA-generation and decision support tools found that most were only usable by scientists, and custom-tailored rather than generic; they concluded a practical and simple tool is required [41]

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